This section details how RNNs process text input sequentially, generating a hidden state representing the entire text at the final time step. This encoded representation can then be fed into other neural network layers to perform tasks like text classification or generate text outputs, showcasing the power of RNNs in encoding textual information. Recurrent Neural Networks (RNNs) are pre-trained on next-word prediction. For complex tasks (translation, question answering), the final hidden state, representing the entire input text, is used. LSTMs address vanishing gradients in long sequences by using multiple paths (H and C) for activations and gradients. The C path avoids the squashing effect of tanh, enabling efficient backpropagation. Sigmoid gates control information flow, selectively remembering or forgetting parts of the hidden state. This architecture allows for effective encoding and decoding of long text sequences. The segment addresses the challenge of vanishing gradients in RNNs with tanh activations when processing long sequences. It introduces the concept of creating multiple paths for activations and gradients, drawing parallels with residual networks in convolutional neural networks, to mitigate the vanishing gradient problem and enable efficient training of long sequences. This segment explains how Recurrent Neural Networks (RNNs), initially trained on simpler tasks like next-word prediction, can be fine-tuned for more complex applications such as question answering or machine translation through a transfer learning approach, highlighting the importance of hidden activations in achieving this. This segment delves into the architecture of Long Short-Term Memory (LSTM) networks, explaining how they address the vanishing gradient problem. It details the role of gates (sigmoid layers) in controlling information flow, allowing the network to selectively remember or forget information from previous time steps, leading to improved performance in handling long sequences.
